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X-WR-CALDESC:Events for ARNI
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250205T113000
DTEND;TZID=America/New_York:20250205T130000
DTSTAMP:20260428T054025
CREATED:20250127T152236Z
LAST-MODIFIED:20250127T152236Z
UID:1456-1738755000-1738760400@arni-institute.org
SUMMARY:CTN: Hidenori Tanaka
DESCRIPTION:Hidenori Tanaka \nTitle and Abstract: TBD
URL:https://arni-institute.org/event/ctn-hidenori-tanaka/
LOCATION:Zuckerman Institute- Kavli Auditorium 9th Fl\, 3227 Broadway\, NY
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250207T113000
DTEND;TZID=America/New_York:20250207T130000
DTSTAMP:20260428T054025
CREATED:20250127T152415Z
LAST-MODIFIED:20250127T152743Z
UID:1460-1738927800-1738933200@arni-institute.org
SUMMARY:CTN: Eva Naumann
DESCRIPTION:Title and Abstract: TBD
URL:https://arni-institute.org/event/ctn-eva-naumann/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250210T113000
DTEND;TZID=America/New_York:20250210T130000
DTSTAMP:20260428T054025
CREATED:20250127T152702Z
LAST-MODIFIED:20250127T152702Z
UID:1463-1739187000-1739192400@arni-institute.org
SUMMARY:CTN Monda Lab: Liam Paninski
DESCRIPTION:Title and Abstract: TBD
URL:https://arni-institute.org/event/ctn-monda-lab-liam-paninski/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250218T160000
DTEND;TZID=America/New_York:20250218T164500
DTSTAMP:20260428T054025
CREATED:20250217T150602Z
LAST-MODIFIED:20250217T150623Z
UID:1493-1739894400-1739897100@arni-institute.org
SUMMARY:ARNI Continual Learning Working Group Spring Opening Meeting
DESCRIPTION:From: Tom Zollo\n\n\n\n\n\nIn Y2\, the aim is to use this working group as a launchpad for a larger ARNI continual learning project (which we hope to spawn multiple subprojects and papers).  We hope for this group to tackle issues that are relevant to both modern practitioners and the ARNI mission of connecting artificial and natural intelligence.\n\nAs a potential topic for this project\, we think we might consider the problem of long and short term memory in LLMs.  There has been recent interest from industry labs\, e.g. Google (paper link) and Meta (paper link)\, in fitting an LLM with a long-term neural memory module to complement the short-term memory given by the context window.  Several threads relevant to ARNI could extend from this research direction.  For instance\, we might consider cognitively-inspired benchmarks for LLM memory systems for lifelong learning\, e.g.\, based on human-like tasks that might be difficult for autoregressive models.  Also\, we could explore methodological work in LLM memory mechanisms based on our understanding of natural intelligence.  We are particularly interested in learning about relevant studies in neuroscience and cognitive science that could help constrain and inspire the methodological approaches.  Beyond these\, one could imagine many other related directions of interest to ARNI.\n\n\nZoom: https://columbiauniversity.zoom.us/j/99160043324?pwd=1BvBZBeyB3b8da74wuLsgPCabCVudL.1
URL:https://arni-institute.org/event/arni-continual-learning-working-group-spring-opening-meeting/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250225T150000
DTEND;TZID=America/New_York:20250225T170000
DTSTAMP:20260428T054025
CREATED:20250217T144605Z
LAST-MODIFIED:20250224T195355Z
UID:1490-1740495600-1740502800@arni-institute.org
SUMMARY:ARNI WG Multi-resource-cost optimization of neural network models: Paul Schrater
DESCRIPTION:Title: Control when confidence is costly \nAbstract:\nWe develop a version of stochastic control that accounts for computational costs of inference. Past studies identified efficient coding without control\, or efficient control that neglects the cost of synthesizing information. Here we combine these concepts into a framework where agents rationally approximate inference for efficient control. Specifically\, we study Linear Quadratic Gaussian (LQG) control with an added internal cost on the relative precision of the posterior probability over the world state. This creates a trade-off: an agent can obtain more utility overall by sacrificing some task performance\, if doing so saves enough bits during inference. We discover that the rational strategy that solves the joint inference and control problem goes through phase transitions depending on the task demands\, switching from a costly but optimal inference to a family of suboptimal inferences related by rotation transformations\, each misestimate the stability of the world. In all cases\, the agent moves more to think less. This work provides a foundation for a new type of rational computations that could be used by both brains and machines for efficient but computationally constrained control.\nWe develop a version of stochastic control that accounts for computational costs of inference. Past studies identified efficient coding without control\, or efficient control that neglects the cost of synthesizing information. Here we combine these concepts into a framework where agents rationally approximate inference for efficient control. Specifically\, we study Linear Quadratic Gaussian (LQG) control with an added internal cost on the relative precision of the posterior probability over the world state. This creates a trade-off: an agent can obtain more utility overall by sacrificing some task performance\, if doing so saves enough bits during inference. We discover that the rational strategy that solves the joint inference and control problem goes through phase transitions depending on the task demands\, switching from a costly but optimal inference to a family of suboptimal inferences related by rotation transformations\, each misestimate the stability of the world. In all cases\, the agent moves more to think less. This work provides a foundation for a new type of rational computations that could be used by both brains and machines for efficient but computationally constrained control. \nZoom Link: https://columbiauniversity.zoom.us/j/98244449046?pwd=ZagtGamVQgwy8XrPdXdlzJRbgrXtVj.1
URL:https://arni-institute.org/event/arni-wg-multi-resource-cost-optimization-of-neural-network-models-paul-schrater/
LOCATION:Zuckerman Institute – L3-079\, 3227 Broadway\, New York\, NY\, 10027\, United States
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250226T140000
DTEND;TZID=America/New_York:20250226T150000
DTSTAMP:20260428T054025
CREATED:20250218T211553Z
LAST-MODIFIED:20250218T211606Z
UID:1501-1740578400-1740582000@arni-institute.org
SUMMARY:ARNI Biological Learning Working Group
DESCRIPTION:Ken Miller will be talking about E/I networks & balanced networks and some computational/functional implications\, there’s two papers I’d suggest reading:on balanced amplification: https://www.sciencedirect.com/science/article/pii/S0896627309001287 review of loosely and tightly balanced networks: https://www.sciencedirect.com/science/article/pii/S0896627321005754. \n\nMeeting Link: meet.google.com/nnq-csiy-yah
URL:https://arni-institute.org/event/arni-biological-learning-working-group/
LOCATION:Virtual
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250227T130000
DTEND;TZID=America/New_York:20250227T140000
DTSTAMP:20260428T054025
CREATED:20250221T155402Z
LAST-MODIFIED:20250221T165304Z
UID:1511-1740661200-1740664800@arni-institute.org
SUMMARY:ARNI Continual Learning Project
DESCRIPTION:Followup to discussion in Meeting 1 \nZoom Link: https://columbiauniversity.zoom.us/j/97176853843?pwd=VLZdh6yqHBcOQhdf816lkN5ByIpIsF.1 \n  \n  \n 
URL:https://arni-institute.org/event/arni-continual-learning-project/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250228T153000
DTEND;TZID=America/New_York:20250228T170000
DTSTAMP:20260428T054025
CREATED:20250128T194129Z
LAST-MODIFIED:20250221T200454Z
UID:1474-1740756600-1740762000@arni-institute.org
SUMMARY:ARNI Distinguished Seminar Series: Marlene Behrmann
DESCRIPTION:About Dr. Marlene Behrmann:\nMarlene Behrmann joined the Department of Ophthalmology at the University of Pittsburgh School of Medicine\, where she holds the John and Clelia Sheppard Chair\, in 2022. She also holds the position of Emeritus Professor at Carnegie Mellon University. Dr. Behrmann’s research is concerned with the psychological and neural bases of visual processing\, with specific attention to the mechanisms by which the signals from the eye are transformed into meaningful percepts by the brain. She adopts an interdisciplinary approach combining computational\, neuropsychological and neuroimaging studies with adults and children in health and disease. Examples of her recent studies include investigations of the cortical visual system in paediatric patients following hemispherectomy and identifying mechanisms of plasticity and elucidating the potential for cortical reorganization\, but she has also studied visual cortical function in individuals with inherited retinal dystrophy. Dr. Behrmann was elected a member of the Society for Experimental Psychologists in 2008\, and was inducted into the National Academy of Sciences in 2015\, and into the American Academy of Arts and Sciences in 2019. Dr Behrmann has received many awards including the Presidential Early Career Award for Engineering and Science\, the APA Distinguished Scientific Award for Early Career Contributions and the Fred Kavli Distinguished Career Contributions in Cognitive Neuroscience Award from the Cognitive Neuroscience Society. \nTitle: The development\, hemispheric organization\, and plasticity of high-level vision \nAbstract: \nAdults recognize complex visual inputs\, such as faces and words\, with remarkable speed\, accuracy and ease\, but a full understanding of these abilities is still lacking. Much prior research has favoured a binary separation of faces and words\, with the right hemisphere specialized for the representation of faces\, and the left hemisphere specialized for the representation of words. Close scrutiny of the data\, however\, suggest a more graded and distributed hemispheric organization\, as well as differing hemispheric profiles across individuals. Combining detailed behavioral data with structural and functional imaging data reveals how the distribution of function both within and between the two cerebral hemispheres emerges over the course of development\, and a computational account of this mature organization is offered and tested. Provocatively\, this mature profile is more malleable than previously thought\, and cross-sectional and longitudinal data acquired from individuals with hemispherectomy reveal how a single hemisphere can subserve both visual classes. Together\, the findings support a view of cortical visual organization (and perhaps\, the organization of other functions too) as plastic and dynamic\, both within and between hemispheres. \nLocation: Zuckerman Institute\, Kavli Auditorium 9th Floor (for access to Zuckerman Institute\, please email Lena Mei @ lm3440@columbia.edu 24 hours prior to the event) \nZoom link: https://columbiauniversity.zoom.us/j/96156119664?pwd=PCGPe1UbEzzbIvGnbAdVa8wX5wH9J0.1
URL:https://arni-institute.org/event/arni-distinguished-seminar-series-marlene-behrmann/
LOCATION:Zuckerman Institute- Kavli Auditorium 9th Fl\, 3227 Broadway\, NY
ORGANIZER;CN="ARNI":MAILTO:arni@columbia.edu
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